Hierarchical Individual Naturalistic Functional Brain Networks with Group Consistency Uncovered by a Two-Stage NAS-Volumetric Sparse DBN Framework

The functional magnetic resonance imaging under naturalistic paradigm (NfMRI) showed great advantages in identifying complex and interactive functional brain networks (FBNs) because of its dynamics and multimodal information. In recent years, various deep learning models, such as deep convolutional autoencoder (DCAE), deep belief network (DBN), and volumetric sparse DBN (vsDBN), can obtain hierarchical FBNs and temporal features from fMRI data. Among them, the vsDBN model revealed a good capability in identifying hierarchical FBNs by modeling fMRI volume images. However, because of the high dimensionality of fMRI volumes and the diverse training parameters of deep learning methods, especially the network architecture that is the most critical parameter for uncovering the hierarchical organization of human brain function, researchers still face challenges in designing an appropriate deep learning framework with automatic network architecture optimization to model volumetric NfMRI. In addition, most of the existing deep learning models ignore the group-wise consistency and intersubject variation properties embedded in NfMRI volumes. To solve these problems, we proposed a two-stage neural architecture search (NAS) and vsDBN model (two-stage NAS-vsDBN model) to identify the hierarchical human brain spatiotemporal features possessing both group consistency and individual uniqueness under naturalistic condition. Moreover, our model defined reliable network structure for modeling volumetric NfMRI data via NAS framework, and the group-level and individual-level FBNs and associated temporal features exhibited great consistency. In general, our method well identified the hierarchical temporal and spatial features of the brain function and revealed the crucial properties of neural processes under natural viewing condition.